ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

2026-09-07

AI Summary

ReactVAU通过轻量级快速检测模块、异常感知持久内存和重型慢速推理模块解决实时视频流异常理解中的因果性和效率问题。

Abstract

In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video models satisfy causal access but dilute rare transient anomalies during memory compression and often invoke heavyweight MLLMs uniformly over long normal intervals. React VAU addresses this gap with three synergistic components: a lightweight Fast Detection Module based on Spatial Grid Folding (SGF) for continuous anomaly filtering; an Anomaly-Aware Persistent Memory (AAPM) that protects critical visual cues from temporal decay; and a heavyweight Slow Reasoning Module that remains dormant during normal streams and is awakened only by suspicious events for semantic verification and causal description. Extensive experiments on multiple benchmarks demonstrate that ReactVAU operates under strict streaming constraints while simultaneously achieving competitive performance in both anomaly detection and causal reasoning, alongside significantly enhanced computational efficiency by minimizing heavyweight MLLM invocations. Project page is available at https://huiyuiui.github.io/React_VAU/

Problem

• Video Anomaly Understanding
• Streaming Constraints
• Causal Access
• Temporal Decay
• Real-time Surveillance

Innovation

• Slow-Fast Decoupled Framework
• Spatial Grid Folding (SGF)
• Anomaly-Aware Persistent Memory (AAPM)